Explainable prediction of loan default based on machine learning models  

作　　者：Xu Zhu Qingyong Chu Xinchang Song Ping Hu Lu Peng 

机构地区：[1]School of Management,Wuhan University of Technology,Wuhan,430070,China [2]Research Institute of Digital Governance and Management Decision Innovation,Wuhan University of Technology,Wuhan,430070,China

出　　处：《Data Science and Management》2023年第3期123-133,共11页数据科学与管理（英文）

基　　金：supported by Fundamental Research Funds for the Central Universities(WUT:2022IVA067).

摘　　要：Owing to the convenience of online loans,an increasing number of people are borrowing money on online platforms.With the emergence of machine learning technology,predicting loan defaults has become a popular topic.However,machine learning models have a black-box problem that cannot be disregarded.To make the prediction model rules more understandable and thereby increase the user’s faith in the model,an explanatory model must be used.Logistic regression,decision tree,XGBoost,and LightGBM models are employed to predict a loan default.The prediction results show that LightGBM and XGBoost outperform logistic regression and decision tree models in terms of the predictive ability.The area under curve for LightGBM is 0.7213.The accuracies of LightGBM and XGBoost exceed 0.8.The precisions of LightGBM and XGBoost exceed 0.55.Simultaneously,we employed the local interpretable model-agnostic explanations approach to undertake an explainable analysis of the prediction findings.The results show that factors such as the loan term,loan grade,credit rating,and loan amount affect the predicted outcomes.

关 键 词：Explainable prediction Machine learning Loan default Local interpretable model-agnostic explanations 

分 类 号：H31[语言文字—英语]